Preface

Savaresi, Sergio M.; Poussot-Vassal, Charles; Spelta, Cristiano; Sename, Olivier; Dugard, Luc

doi:10.1016/b978-0-08-096678-6.00019-5

Cited by 34 publications

(66 citation statements)

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“…Therefore a diversity of control strategies for semi-active dampers is presented in literature. An overview about these control strategies can be found in [20] and [21]. As most of these strategies need state feedback, but not all states can be measured, state estimation becomes an important topic during the design of semi-active suspension systems.…”

Section: Example: Vehicle Vertical Dynamics States Estimationmentioning

confidence: 99%

Nonlinear State Estimation with an Extended FMI 2.0 Co-Simulation Interface

Brembeck

Pfeiffer

Fleps-Dezasse

et al. 2014

Linköping Electronic Conference Proceedings

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In this paper we propose a method how to automatically utilize continuous-time Modelica models directly in nonlinear state estimators. The approach is based on an extended FMI 2.0 Co-Simulation Interface [1] that interacts with the state estimation algorithms implemented in a Modelica library [2]. Besides a short introduction to Kalman Filter based state estimation, we give details on a generic interface to cooperate with FMUs in Modelica, an implementation of nonlinear state estimation based on this interface, and the Dymola prototype used for the evaluation. Finally we show first results in a tire load estimation application [3] for DLR's robotic electric research platform ROMO [4].

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Section: Example: Vehicle Vertical Dynamics States Estimationmentioning

confidence: 99%

Nonlinear State Estimation with an Extended FMI 2.0 Co-Simulation Interface

Brembeck

Pfeiffer

Fleps-Dezasse

et al. 2014

Linköping Electronic Conference Proceedings

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“…The actual damping coefficient c always remains in that interval: 0 ≤ c min ≤ c ≤ c max , where c min and c max are the shockabsorber technological limitations. This last limitation is the so-called "passivity-constraint" of a semi-active suspension -guaranteeing that the actuator only dissipates energy [9], [4], [5]. Since the control signal c in modifies the damping coefficient c, a state variable, the model (1) is nonlinear.…”

Section: A Nonlinear Model Descriptionmentioning

confidence: 99%

“…[1], [2], [3], [4], [5], [6], [7], [8], [9]), semi-active suspensions have received a lot of attention since they seem to provide the best compromise between cost (energy-consumption, actuators/sensors hardware) and performance. The concept of semi-active suspensions can be applied over a wide range of application domains: roadvehicle, cabin in trucks or tractors, seat, trains, appliances (e.g.…”

Section: Introduction a Motivations And Frameworkmentioning

confidence: 99%

A methodology for optimal semi-active suspension systems performance evaluation

Poussot-Vassal¹,

Savaresi

Spelta

et al. 2010

49th IEEE Conference on Decision and Control (CDC)

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Abstract-This paper concerns the study of the optimal performance computation of a semi-active suspension evaluated in terms of comfort and handling performances. To this aim the semi-active suspension system is described as a quartercar model, equipped with a controllable damper, providing an effective representation of the semi-active suspension dynamics. The passive suspension presents dual performances: at low frequency the better comfort performances are ensured by a high damped suspension, whereas the best comfort damping is low at mid and high frequency. The handling performance is perfectly the opposite. This study highlights how this trade-off can be overcome with an optimal control of damping and how the best performances in terms of comfort are reflected in terms of handling and vice verse. The main result of this paper is to propose a methodology allowing for evaluation of handling and comfort lower theoretical filtering bounds, which can be used as a benchmark for any semi-active control design.

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“…The conditions (9) and (10) have the same structure as (13), therefore the reconstruction can be carried out (11). Thus, the next optimization problem is formed to find the maximum Controlled Invariant Set:…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The summary of the semi-active suspension control considering the comfort criterion is presented in [13], [14]. Sky-Hook and clipped control design laws based on model predictive control technique are used in [15].…”

Section: Analysis Of Suspension Performancesmentioning

confidence: 99%

The impact of suspension control on the controllability of the lateral vehicle dynamics

Gáspár

Németh

Bokor

et al. 2016

2016 IEEE 55th Conference on Decision and Control (CDC)

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Abstract-Since there is a coupling between lateral and vertical dynamics, the interactions between control components must be taken into consideration. The paper presents the effects of vertical load variations on the controlled invariant set of the steering system. In the model the nonlinear characteristics of the tire force are approximated by the polynomial form. The analysis is based on Sum-of-Squares programming method and parameter-dependent polynomial control Lyapunov functions. The Maximum Controlled Invariant Sets of the steering as a function of vertical loads are illustrated through a simulation example. The results of the analysis are built into the control design of the suspension system. A semi-active suspension system using preview control is applied. The operation of the controller is illustrated through simulation examples.

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Preface

Cited by 34 publications

References 0 publications

Nonlinear State Estimation with an Extended FMI 2.0 Co-Simulation Interface

Nonlinear State Estimation with an Extended FMI 2.0 Co-Simulation Interface

A methodology for optimal semi-active suspension systems performance evaluation

The impact of suspension control on the controllability of the lateral vehicle dynamics

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